| 乔文超,王红雨,王鸿东.基于 BP 神经网络的无人机IMU 多传感器冗余的补偿算法[J].电子测量与仪器学报,2020,34(12):19-28 |
| 基于 BP 神经网络的无人机IMU 多传感器冗余的补偿算法 |
| Compensation algorithm for UAV IMU multi-sensor redundancy based on BP neural network |
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| DOI： |
| 中文关键词: 惯性测量多传感器冗余 神经网络 数据融合 仲裁 IMU 冗余安装 |
| 英文关键词:IMU multi-sensor redundant neural network data fusion arbitration IMU redundant installation |
| 基金项目:国家自然科学基金(61471237,11174206)项目资助 |
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| 摘要点击次数: 315 |
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| 中文摘要: |
| 针对无人机多传感器数据决策时存在的数据可靠性不足以及资源浪费的问题,提出一种基于 BP 神经网络的无人机惯
性测量单元(IMU)多传感器冗余的补偿算法。 将低精度的 IMU 传感器数据输入到 BP 神经网络,利用 BP 神经网络的非线性拟
合能力,补偿低精度 IMU 数据的误差,然后利用基于置信度的数据仲裁算法对多个较高精度数据进行仲裁,输出经过数据融合
后的传感器数据,此过程还可以进行传感器故障判断和定位。 通过改变同类型传感器安装方式解决奇点问题。 实验结果表明,
经过神经网络误差补偿后,误差比原来减小了 55. 2%,比使用卡尔曼滤波算法进行误差补偿后的误差小 53. 9%。 此算法充分发
挥了冗余传感器设计的优势,提高了传感器系统的可靠性。 |
| 英文摘要: |
| Aiming at the problems of insufficient data reliability and resource waste in the decision of redundant data of UAVs, a
compensation algorithm for UAV IMU multi-sensor redundancy based on BP neural networks is proposed. The low-precision IMU sensor
data is input to the BP neural network, and the non-linear fitting capability of the BP neural network is used to compensate for errors in
low-precision IMU data, then use data arbitration algorithm based on confidence to arbitrate multiple higher-precision data and output the
sensor data after data fusion. This process can also judge and locate sensor faults. The singularity problem can be solved by changing the
installation method of similar sensors. The experimental results prove that after neural network error compensation, the error is reduced
by 55. 2%. Furthermore, the error after neural network error competition is 53. 9% smaller than the error after using the kalman filter
algorithm for error compensation. The algorithm takes full advantage of redundant sensor design, improves the reliability of the sensor
system. |
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